Andreas Kranis

Background: Pedigree-based studies of germline mutations provide a direct understanding of the origin and consequences of genetic variation, and allow quantification of how mutations affect functional variation. Here, we use whole-genome sequencing data from 194 chicken ( Gallus gallus ) trios from a commercial pedigree line to estimate the germline mutation rate and spectrum, and to characterize patterns of mutational variation across the genome and genes, aiming to rank protein coding genes by their expected contributions to mutational variance ( V _m ). Results: We estimated a mutation rate of µ = 3.49 × 10 ⁻⁹ mutations per nucleotide site per generation. A goodness-of-fit test (χ ² ₁₃ = 10.97, P = 0.61) indicated that the number of new mutations per generation follows a Poisson distribution ( λ = 6.22). A read-based phasing approach revealed a strong sex bias, with males contributing approximately twice as many mutations as females. Microchromosomes showed a point estimate of mutation rate approximately 8% higher than macrochromosomes, likely owing to their higher GC content and a mutation spectrum dominated by C→T transitions. From this spectrum, we built a codon transition matrix and show that genes vary widely in their propensity to mutation based on codon composition, likely reflecting large differences in their contributions to V _m . Specifically, we found that genes involved in immunity and development rank among those most prone to non-silent mutations. Conclusions: Our results reveal predictable patterns of gene-level mutational variation and provide a framework for anticipating how new mutations generate functional variation. By bridging quantitative genetics and molecular biology, we provide a mechanistic basis for variation in V _m across the genome, showing that heterogeneity in V _m can arise from gene-specific features, particularly codon composition and mutational target size. These metrics offer a way to rank genes and prioritize genetic variants by their expected contributions to biological functions as a consequence of de novo mutation. This provides a new basis for informed management of genetic variation in breeding populations and for understanding the evolutionary dynamics of functionally important genes.

The current study investigated the genomic selection accuracies and biases estimates from two commercial Pekin duck lines reared under commercial breeding practices. A large dataset of 26K duck records comprising both phenotype and imputed genotype information (60K chip) were analysed for growth, welfare and primary feather length traits. First, we employed mixed linear models with relationship matrices computed from the pedigree (BLUP) or markers (GBLUP) to estimate the variance components and breeding values. Then, we estimated the selection accuracies and selection biases to assess the more appropriate models. Our results showed moderately high imputation accuracies of 0.93 and 0.92 for lines A and D respectively. In both lines, the heritability estimates obtained using the pedigree were generally higher than using genomic markers in all traits considered. These ranged for juvenile weight (JW) from 0.22±0.01 vs 0.25±0.01 in line A vs line D using marker information to 0.39±0.02 to 0.50±0.02 using the pedigree in line A vs line D for slaughter body weight (BW). We observed very low estimates of heritability for gait 0.07±0.01 using markers in both lines. Breast muscle depth (BD) also had lower estimates of 0.15-0.16 using markers. For line A, the genomic predictions were generally higher when using the G-matrix than the A-matrix with the highest prediction was for BW (r ² =0.68-0.70) and JW with r ² of 0.49. The estimates for gait and foot pad dermatitis (FPD) were greatly improved by using the G-Matrix at 0.58 vs 0.24 and 0.68 vs 0.44 respectively for markers vs pedigree information. For line D, the same improvements for G-Matrix vs A-Matrix were observed with estimates for BD being similar in the two lines. However, for BD the G-Matrix greatly improved the estimates from 0.50 to 0.71 unlike in line A where they remained at 0.50. The bias in line A were minimal (0.01- 0.19) using the G-Matrix compared to 0.02- 0.41 when using A-Matrix. The highest observed bias was for JW followed by BD for the G-matrix whereas when using the A-matrix we observed higher biases in many traits (JW, BW, BD and gait). The biases for line D were generally lower for the G-matrix (0.02 - 0.17 vs 0.00 - 0.19) than those observed in line A using markers whereas higher biases were observed using the pedigree (0.01 - 0.37). Current findings pinpointed that all traits were heritable with higher prediction accuracies and lower biases when using GBLUP as opposed to traditional BLUP. The present study demonstrates the effectiveness of GBLUP for improving prediction accuracy and reducing bias in selection traits of Pekin ducks, particularly for traits with low heritability. Author Summary: The study explored genomic selection in two commercial Pekin duck lines. Using a large dataset of 26,000 records, including phenotype and genotype data, researchers analyzed growth, welfare, and feather length traits. They applied statistical models to assess variance components and breeding values, comparing traditional pedigree-based methods (BLUP) with genomic marker-based methods (GBLUP). Results showed high imputation accuracies (93% for line A and 92% for line D). Heritability estimates varied, with genomic markers generally producing lower estimates than pedigrees, except for traits like gait and breast muscle depth where genomic predictions were superior. For example, line A showed higher accuracy using genomic data for body weight and juvenile weight. Overall, genomic predictions (GBLUP) provided higher accuracy and lower bias compared to traditional methods, especially for traits with low heritability. This highlights the effectiveness of GBLUP in improving selection processes in Pekin ducks.

Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a “double trait covariances” analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999–2001 to 2020–2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.

This paper provides a comprehensive overview of the history of commercial poultry breeding, from domestication to the development of science and commercial breeding structures. The development of breeding goals over time, from mainly focusing on production to broad goals, including bird welfare and health, robustness, environmental impact, biological efficiency and reproduction, is detailed. The paper outlines current breeding goals, including traits (e.g., on foot and leg health, contact dermatitis, gait, cardiovascular health, robustness and livability), recording techniques, their genetic basis and how trait these antagonisms, for example, between welfare and production, are managed. Novel areas like genomic selection and gut health research and their current and potential impact on breeding are highlighted. The environmental impact differences of various genotypes are explained. A future outlook shows that balanced, holistic breeding will continue to enable affordable lean animal protein to feed the world, with a focus on the welfare of the birds and a diversity of choice for the various preferences and cultures across the world.

Background: Body weight (BW) is an economically important trait in the broiler (meat-type chickens) industry. Under the assumption of polygenicity, a “large” number of genes with “small” effects is expected to control BW. To detect such effects, a large sample size is required in genome-wide association studies (GWAS). Our objective was to conduct a GWAS for BW measured at 35 days of age with a large sample size. Methods: The GWAS included 137,343 broilers spanning 15 pedigree generations and 392,295 imputed single nucleotide polymorphisms (SNPs). A false discovery rate of 1% was adopted to account for multiple testing when declaring significant SNPs. A Bayesian ridge regression model was implemented, using AlphaBayes, to estimate the contribution to the total genetic variance of each region harbouring significant SNPs (1 Mb up/downstream) and the combined regions harbouring non-significant SNPs. Results: GWAS revealed 25 genomic regions harbouring 96 significant SNPs on 13 Gallus gallus autosomes (GGA1 to 4, 8, 10 to 15, 19 and 27), with the strongest associations on GGA4 at 65.67–66.31 Mb (Galgal4 assembly). The association of these regions points to several strong candidate genes including: (i) growth factors (GGA1, 4, 8, 13 and 14); (ii) leptin receptor overlapping transcript ( LEPROT )/leptin receptor ( LEPR ) locus (GGA8), and the STAT3 / STAT5B locus (GGA27), in connection with the JAK/STAT signalling pathway; (iii) T-box gene ( TBX3/TBX5 ) on GGA15 and CHST11 (GGA1), which are both related to heart/skeleton development); and (iv) PLAG1 (GGA2). Combined together, these 25 genomic regions explained ~ 30% of the total genetic variance. The region harbouring significant SNPs that explained the largest portion of the total genetic variance (4.37%) was on GGA4 (~ 65.67–66.31 Mb). Conclusions: To the best of our knowledge, this is the largest GWAS that has been conducted for BW in chicken to date. In spite of the identified regions, which showed a strong association with BW, the high proportion of genetic variance attributed to regions harbouring non-significant SNPs supports the hypothesis that the genetic architecture of BW35 is polygenic and complex. Our results also suggest that a large sample size will be required for future GWAS of BW35.

Campylobacter is the leading cause of bacterial foodborne gastroenteritis in many countries. Source attribution studies unequivocally identify the handling or consumption of contaminated poultry meat as the primary risk factor. One potential strategy to control Campylobacter is to select poultry with increased resistance to colonisation. We conducted genomic and transcriptomic analyses of commercial pedigree broilers exposed to Campylobacter to examine persistent colonisation of the caecum as a quantitative trait. 3,000 broilers were genotyped using a 50K single nucleotide polymorphism (SNP) array and imputed to 600K SNPs. Genotypes were analysed for associations with the number of viable Campylobacter in the caeca. Heritability of the trait was modest but significantly greater than zero (h ² =0.11 ± 0.03). Genome-wide association analyses confirmed quantitative trait loci (QTL) on chromosomes 14 and 16 previously identified using the progeny of crosses of inbred lines differing in resistance, and detected two additional genome-wide significant QTLs on chromosomes 19 and 26. RNA-Seq analysis of the transcriptome of caecal tonsils from birds at the low and high extremes of C. jejuni colonisation phenotype identified differentially transcribed genes, mainly located within the QTL on chromosome 16 and proximal to the major histocompatibility complex (MHC) locus. We also identified strong cis -QTLs located within the MHC suggesting the presence of cis -acting variation in both MHC class I, class II and BG genes. Multiple other cis- acting variants were identified in association with key immune genes ( COPS3, CCL4, CR1L, C4BP, PLGR ) in the other QTLs. Pathway and network analysis implicated cooperative functional pathways and networks in colonisation, including those related to antigen presentation, innate and adaptive immune responses, calcium, and renin-angiotensin signalling. While co-selection for enhanced resistance and other breeding goal traits is feasible, the frequency of resistance-associated alleles was high in the population studied and non-genetic factors significantly influence Campylobacter colonisation in poultry. Author summary: Campylobacter infection is estimated to cause 95 million illnesses in people worldwide each year. Human infections mostly involve gastroenteritis, but can have severe complications. The handling or consumption of contaminated poultry meat is a key risk factor for human campylobacteriosis. The bacteria reach high numbers in the intestines of chickens reared for meat (broilers) and are frequently found on carcasses after slaughter. Effective vaccines against Campylobacter are not yet available, and treatments to reduce carcass contamination (e.g. chlorination) are not acceptable in some markets. One alternative is to breed for chickens with improved resistance to Campylobacter colonisation. To test the feasibility of this option in commercial birds, we analysed the genetic make-up of 3,000 pedigree broilers and determined the number of Campylobacter in their gut. There were associations between specific regions of the chicken genome and resistance to Campylobacter . Within some of these regions, expression of certain genes differed between birds at the low and high extremes of Campylobacter colonisation, providing a potential explanation for genetic variation in resistance. Selection of poultry with increased resistance to Campylobacter colonisation may be a complementary strategy to improved biosecurity, management, handling and processing procedures to reduce the burden of Campylobacter on human health.

Aim of the present study was to investigate whether body weight (BW) in broilers is associated with functional modular genes. To this end, first a GWAS for BW was conducted using 6,598 broilers and the high density SNP array. The next step was to search for positional candidate genes and QTLs within strong LD genomic regions around the significant SNPs. Using all positional candidate genes, a network was then constructed and community structure analysis was performed. Finally, functional enrichment analysis was applied to infer the functional relevance of modular genes. A total number of 645 positional candidate genes were identified in strong LD genomic regions around 11 genome-wide significant markers. 428 of the positional candidate genes were located within growth related QTLs. Community structure analysis detected 5 modules while functional enrichment analysis showed that 52 modular genes participated in developmental processes such as skeletal system development. An additional number of 14 modular genes (GABRG1, NGF, APOBEC2, STAT5B, STAT3, SMAD4, MED1, CACNB1, SLAIN2, LEMD2, ZC3H18, TMEM132D, FRYL and SGCB) were also identified as related to body weight. Taken together, current results suggested a total number of 66 genes as most plausible functional candidates for the trait examined.

Background: Phenotypic networks describing putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effects in genome-wide association studies (GWAS). In GWAS with multiple phenotypes, reconstructing underlying causal structures among traits and SNPs using a single statistical framework is essential for understanding the entirety of genotype-phenotype maps. A structural equation model (SEM) can be used for such purposes. Methods: We applied SEM to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships among body weight (BW), breast meat (BM), hen-house production (HHP), and SNPs. We assessed the performance of SEM-GWAS by comparing the model results with those obtained from traditional multi-trait association analyses (MTM-GWAS). Results: Three different putative causal path diagrams were inferred from highest posterior density (HPD) intervals of 0.75, 0.85, and 0.95 using the inductive causation algorithm. A positive path coefficient was estimated for BM→BW, and negative values were obtained for BM→HHP and BW→HHP in all implemented scenarios. Further, the application of SEM-GWAS enabled the decomposition of SNP effects into direct, indirect, and total effects, identifying whether a SNP effect is acting directly or indirectly on a given trait. In contrast, MTM-GWAS only captured overall genetic effects on traits, which is equivalent to combining the direct and indirect SNP effects from SEMGWAS. Conclusions: Although MTM-GWAS and SEM-GWAS use the same probabilistic models, we provide evidence that SEM-GWAS captures complex relationships and delivers a more comprehensive understanding of SNP effects compared to MTM-GWAS. Our results showed that SEM-GWAS provides important insight regarding the mechanism by which identified SNPs control traits by partitioning them into direct, indirect, and total SNP effects.

Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations. A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation. Estimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together. Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.